Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms Article Swipe
Nagnath Aherwadi
,
Usha Mittal
,
Jimmy Singla
,
N. Z. Jhanjhi
,
Abdulsalam Yassine
,
M. Shamim Hossain
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11244100
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics11244100
Fruit that has reached maturity is ready to be harvested. The prediction of fruit maturity and quality is important not only for farmers or the food industry but also for small retail stores and supermarkets where fruits are sold and purchased. Fruit maturity classification is the process by which fruits are classified according to their maturity in their life cycle. Nowadays, deep learning (DL) has been applied in many applications of smart agriculture such as water and soil management, crop planting, crop disease detection, weed removal, crop distribution, strong fruit counting, crop harvesting, and production forecasting. This study aims to find the best deep learning algorithms which can be used for the prediction of fruit maturity and quality for the shelf life of fruit. In this study, two datasets of banana fruit are used, where we create the first dataset, and the second dataset is taken from Kaggle, named Fruit 360. Our dataset contains 2100 images in 3 categories: ripe, unripe, and over-ripe, each of 700 images. An image augmentation technique is used to maximize the dataset size to 18,900. Convolutional neural networks (CNN) and AlexNet techniques are used for building the model for both datasets. The original dataset achieved an accuracy of 98.25% for the CNN model and 81.75% for the AlexNet model, while the augmented dataset achieved an accuracy of 99.36% for the CNN model and 99.44% for the AlexNet model. The Fruit 360 dataset achieved an accuracy of 81.96% for CNN and 81.75% for the AlexNet model. We concluded that for all three datasets of banana images, the proposed CNN model is the best suitable DL algorithm for bananas’ fruit maturity classification and quality detection.
Related Topics
Concepts
Convolutional neural network
Maturity (psychological)
Deep learning
Artificial intelligence
Computer science
Machine learning
Crop
Capability Maturity Model
Agricultural engineering
Artificial neural network
Agronomy
Engineering
Biology
Programming language
Developmental psychology
Software
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics11244100
- https://www.mdpi.com/2079-9292/11/24/4100/pdf?version=1670580533
- OA Status
- gold
- Cited By
- 131
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311974999
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311974999Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics11244100Digital Object Identifier
- Title
-
Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-09Full publication date if available
- Authors
-
Nagnath Aherwadi, Usha Mittal, Jimmy Singla, N. Z. Jhanjhi, Abdulsalam Yassine, M. Shamim HossainList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics11244100Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/11/24/4100/pdf?version=1670580533Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/11/24/4100/pdf?version=1670580533Direct OA link when available
- Concepts
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Convolutional neural network, Maturity (psychological), Deep learning, Artificial intelligence, Computer science, Machine learning, Crop, Capability Maturity Model, Agricultural engineering, Artificial neural network, Agronomy, Engineering, Biology, Programming language, Developmental psychology, Software, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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131Total citation count in OpenAlex
- Citations by year (recent)
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2025: 75, 2024: 36, 2023: 20Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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